CN106446113A - Mobile big data analysis method and device - Google Patents
Mobile big data analysis method and device Download PDFInfo
- Publication number
- CN106446113A CN106446113A CN201610828017.5A CN201610828017A CN106446113A CN 106446113 A CN106446113 A CN 106446113A CN 201610828017 A CN201610828017 A CN 201610828017A CN 106446113 A CN106446113 A CN 106446113A
- Authority
- CN
- China
- Prior art keywords
- web page
- page contents
- online
- url address
- signaling data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
Abstract
The invention provides a mobile big data analysis method and device which are used for conducting big data analysis on online behaviors of a mobile terminal user. The method comprises the steps that online original signaling data is collected; the online original signaling data is analyzed through a deep packet inspection technology, and URL addresses accessed by the mobile terminal user on the internet are obtained; the URL addresses are classified and deeply analyzed, and key fields are obtained from the URL addresses; webpage content analysis is conducted according to the key fields, and the online behaviors of the user are obtained by combining personnel information of the user in the online original signaling data, wherein the online behaviors comprise a terminal used for surfing the internet, the internet location, application software used for surfing the internet, webpage operation behavior types, viewed content types, network type and internet user identity. In this way, the online behaviors can be more comprehensively and accurately reflected, the calculated amount is smaller, and reliable support is provided for later data statistic analysis.
Description
Technical field
The present invention relates to mobile Internet access Data Data analysis field, in particular to a kind of based on mobile network's online
The mobile big data analysis method of data and device.
Background technology
Service on net operator is operator is natural big data company, has the user data of magnanimity, such as surfs the Net
Behavioral data, web transaction data, position data, network management data, signaling data, microblog data, instant communication data, webpage,
Sensing data, voice data, video file, picture, daily record, monitor in real time video etc..And the data of operator reliability,
Integrality, mobility, real-time aspect have the advantage of oneself uniqueness.By to the excavation of operator's mass data, shared, analysis
Have become as operation inside carrier service, service government, service enterprise, the valuable source servicing the common people and realization means.
In traditional Internet data analysis mode, it is only capable of extracting simple information in user's Internet data information, analysis
Not comprehensive, analyze a situation single it is impossible to reflect the behavior of user's online exactly, had a strong impact on the statistical of later data
Analysis.
Content of the invention
In order to overcome above-mentioned deficiency of the prior art, the technical problem to be solved is that offer one kind can be more
The mobile big data analysis method and device of the internet behavior accurately comprising in analysis user's Internet data.
For method, a kind of mobile big data analytic method that the present invention provides, it is applied to mobile phone users
Internet behavior carries out big data analysis, and methods described includes：
The original signaling data of online of collection user.
Using deep packet inspection technical, described the carrying out surfing the Net original signaling data is parsed, obtain on mobile phone users
The URL address that net accesses.
Is classified and depth analysis in described URL address, obtained critical field from described URL address.
Text mining is carried out according to described critical field, in conjunction with the described individual subscriber surfed the Net in original signaling data
Information, the internet behavior of acquisition user, wherein, described internet behavior includes surfing the Net used by terminal used, online position, online
Application software, web page operation behavior type, browsing content type, intranet network type or Internet user's identity.
Further, in above-mentioned mobile big data analytic method, described web page contents are carried out according to described critical field
The step of analysis includes：
Content according to described critical field mates corresponding user operation or net in default web page contents rule base
Page content, includes described user operation or web page contents pass corresponding with described keyword to described web page contents rule base
System.
According to described user operation or web page contents, in conjunction with the described userspersonal information surfing the Net in original signaling data,
Obtain the internet behavior of user.
Further, in above-mentioned mobile big data analytic method, methods described also includes：
Set up described web page contents rule base, wherein, described web page contents rule base is stored with critical field in URL address
Matching relationship with the described user operation in this corresponding webpage in URL address or web page contents.
Further, in above-mentioned mobile big data analytic method, the described step setting up described web page contents rule base
Also include：
Described URL address is carried out with sampling and obtains URL address sample, described acquisition URL address is crawled by web crawlers
The corresponding info web of sample, user operation or web page contents that storage user executes on this webpage, and set up described URL ground
In location, critical field and the corresponding relation of described user operation or web page contents, preserve described corresponding relation to described web page contents
Rule base.
Further, in above-mentioned mobile big data analytic method, the described step setting up described web page contents rule base
Also include：
Sampling is carried out to the original signaling data of described online and obtains original signaling data sample of surfing the Net.
User operation in the reduction original signaling data sample of online or web page contents, and obtain the original signaling of described online
The critical field of URL address in data sample.
Described user operation or web page contents are preserved to described web page contents rule with the corresponding relation of this critical field
Storehouse.
For device, the present invention also provides a kind of mobile big data resolver, is applied to mobile phone users
Internet behavior carries out big data analysis, and described device includes：
Original signaling data acquisition module, the original signaling data of the online for gathering user.
Deep-packet detection module, for being solved to described the carrying out surfing the Net original signaling data using deep packet inspection technical
Analysis, obtains the URL address that mobile phone users online accesses.
URL address depth analysis module, for being classified and depth analysis to described URL address, from described URL address
Obtain critical field.
Internet behavior analysis module, for text mining is carried out according to described critical field, former in conjunction with described online
Userspersonal information in beginning signaling data, obtains the internet behavior of user, and wherein, described internet behavior is included used by online
Terminal, online position, online used by application software, web page operation behavior type, browsing content type, intranet network type or on
Network users identity.
Further, in above-mentioned mobile big data resolver, described internet behavior analysis module includes：
Webpage matched sub-block, regular in default web page contents according to the content of described critical field for critical field
Mate corresponding user operation or web page contents in storehouse, described user operation or webpage are included to described web page contents rule base
Content and the corresponding relation of described keyword.
Content analysis submodule, for according to described user operation or web page contents, in conjunction with the original signaling number of described online
According in userspersonal information, obtain user internet behavior.
Further, in above-mentioned mobile big data resolver, described device also includes：
Web page contents rule base sets up module, is used for setting up described web page contents rule base, wherein, described web page contents rule
Then stock contain critical field and the described user operation in this corresponding webpage in URL address in URL address or web page contents
Join relation.
Further, in above-mentioned mobile big data resolver, described web page contents rule base is set up module and is additionally operable to：
Described URL address is carried out with sampling and obtains URL address sample, described acquisition URL address is crawled by web crawlers
The corresponding info web of sample, user operation or web page contents that storage user executes on this webpage, and set up described URL ground
In location, critical field and the corresponding relation of described user operation or web page contents, preserve described corresponding relation to described web page contents
Rule base.
Further, in above-mentioned mobile big data resolver, described web page contents rule base is set up mould and is additionally operable to：
Sampling is carried out to the original signaling data of described online and obtains original signaling data sample of surfing the Net.The reduction original letter of online
Make the user operation in data sample or web page contents, and obtain the pass of URL address in described original signaling data sample of surfing the Net
Key field.Described user operation or web page contents are preserved to described web page contents rule with the corresponding relation of this critical field
Storehouse.
In terms of existing technologies, the invention has the advantages that：
A kind of mobile big data analysis method and device that the present invention provides, deep packet inspection technical is original to described online
The carrying out of signaling data parses, and obtains the URL address that mobile phone users online accesses.And by the pass in described URL address
Key word mates user operation or web page contents in webpage in default described web page contents rule base, is analyzed with this and is used
The internet behavior at family.So, can more comprehensive and accurate reflection user's online behavior, and amount of calculation is less, is the later stage
Data statistic analysis provide to be supported.
Brief description
In order to be illustrated more clearly that the technical scheme of the embodiment of the present invention, below will be attached to use required in embodiment
Figure is briefly described it will be appreciated that the following drawings illustrate only certain embodiments of the present invention, and it is right to be therefore not construed as
The restriction of scope, for those of ordinary skill in the art, on the premise of not paying creative work, can also be according to this
A little accompanying drawings obtain other related accompanying drawings.
Fig. 1 is applied environment schematic diagram provided in an embodiment of the present invention；
Fig. 2 is the structured flowchart of data analyzing device provided in an embodiment of the present invention；
Fig. 3 is big data analytic method schematic flow sheet provided in an embodiment of the present invention；
Fig. 4 is the sub-process schematic diagram of step S140 of the present invention；
Fig. 5 is big data resolver structured flowchart provided in an embodiment of the present invention.
Icon：100- DAF；110- moves big data resolver；120- memory；130- processor；
111- original signaling data acquisition module；112- deep-packet detection module；113-URL address depth analysis module；114- surfs the Net
Behavioural analysis module；1141- webpage matched sub-block；1142- content analysis submodule；115- web page contents rule base sets up mould
Block；200- user terminal；300- carrier server；400- network.
Specific embodiment
Purpose, technical scheme and advantage for making the embodiment of the present invention are clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described it is clear that described embodiment is
The a part of embodiment of the present invention, rather than whole embodiments.The present invention generally described and illustrated in accompanying drawing herein is implemented
The assembly of example can be arranged with various different configurations and design.
Therefore, below the detailed description of the embodiments of the invention providing in the accompanying drawings is not intended to limit claimed
The scope of the present invention, but be merely representative of the selected embodiment of the present invention.Based on the embodiment in the present invention, this area is common
The every other embodiment that technical staff is obtained under the premise of not making creative work, broadly falls into the model of present invention protection
Enclose.
It should be noted that：Similar label and letter represent similar terms in following accompanying drawing, therefore, once a certain Xiang Yi
It is defined in individual accompanying drawing, then do not need it to be defined further and explains in subsequent accompanying drawing.
In describing the invention, it should be noted that term " first ", " second ", " the 3rd " etc. are only used for differentiation and retouch
State, and it is not intended that indicating or hint relative importance.
A kind of mobile big data analysis method and device that the present embodiment provides is applied to the online to mobile phone users
Behavior is carried out on the DAF 100 of big data analysis.Refer to Fig. 1, Fig. 1 passes through for described DAF 100
Network 400 and user terminal 200 and carrier server 300 interact schematic diagram.
Refer to Fig. 2, the structured flowchart of the DAF 100 shown in Fig. 1.Described DAF 100 includes
Mobile big data resolver 110, memory 120, processor 130.
Described memory 120, processor 130 and each element are directly or indirectly electrically connected with, each other to realize number
According to transmission or interaction.For example, these elements can be realized electrically by one or more communication bus or holding wire each other
Connect.Described mobile big data resolver 110 is included at least one and can be stored in the form of software or firmware (firmware)
In the described memory 120 or be solidificated in described DAF 100 operating system (operating system, OS) in
Software function module.Described processor 130 is used for executing the executable module of storage in described memory 120, for example described
Software function module included by mobile big data resolver 110 and computer program etc..
Wherein, described memory 120 may be, but not limited to, random access memory (Random Access
Memory, RAM), read-only storage (Read Only Memory, ROM), programmable read only memory (Programmable
Read-Only Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only
Memory, EPROM), electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only
Memory, EEPROM) etc..Wherein, memory 120 be used for storage program, described processor 130 after receiving execute instruction,
Execution described program.
Refer to Fig. 3, what Fig. 3 position the present embodiment provided is applied to a kind of mobile big of DAF 100 described in Fig. 2
Data analysis method, the method comprising the steps of.
Step S110, the original signaling data of online of collection user.
Specifically, in the present embodiment, described surf the Net original signaling data include user use mobile Internet access terminal with
The initial data of interaction between the mobile Internet access service provider communication server.
Step S120, is parsed to described the carrying out surfing the Net original signaling data using deep packet inspection technical, obtains and move
The URL address that terminal use's online accesses.
Specifically, in the present embodiment, by deep-packet detection (Deep Packet Inspection, DPI) technology pair
Described the carrying out surfing the Net original signaling data parses.Deep packet inspection technical is in traditional IP data packet inspection technical, that is, exist
On the basis of the detection and analysis of the packet elements that OSI layer 2 comprises between the 4th layer, increased to application layer data
Protocol identification, packet content detection and depth decoding.
Step S130, is classified and depth analysis to described URL address, obtains critical field from described URL address.
Multiple fields are comprised, some of them field comprises the content in the corresponding webpage in this URL address in URL address.As used
When family executes hunting action in webpage, in the URL address of link, comprise the search command critical field that implication is " search ", and
User searches for the critical field of content.Classified in the URL address that described DAF 100 links to user and depth is divided
Analysis, extracts the critical field in URL address, and the incidence relation of each critical field.
Step S140, carries out text mining according to described critical field, in conjunction with the original signaling data of described online
Userspersonal information, obtain the internet behavior of user, wherein, described internet behavior includes surf the Net terminal used, online position
Put, application software used of surfing the Net, web page operation behavior type, the content browsing webpage, intranet network type or Internet user's body
Part.
Specifically, refer to Fig. 4, step S140 can include following sub-step in the present embodiment.
Sub-step S141, the content according to described critical field mates corresponding use in default web page contents rule base
Family operation or web page contents, include described user operation or web page contents and described keyword to described web page contents rule base
Corresponding relation.
Further, in this embodiment, methods described can also include：Set up described web page contents rule base.
In an embodiment of the present embodiment, the described step setting up web page contents rule base includes：
Described URL address is carried out with sampling and obtains URL address sample, described acquisition URL address is crawled by web crawlers
The corresponding info web of sample, user operation or web page contents that storage user executes on this webpage, and set up described URL ground
In location, critical field and the corresponding relation of described user operation or web page contents, preserve described corresponding relation to described web page contents
Rule base.
Specifically, the part URL address obtaining in sampling selecting step S120, obtains described URL geological sample.By net
Network reptile is linked to the corresponding website of described URL address sample, automatically extracts the content in website, replicates and return during execution
The user operation of execution or web page contents on shelves and preservation webpage.Obtain by the described user operation on this webpage or webpage
Hold and set up corresponding relation with the critical field in the described URL address obtaining in step s 130.As keyword in URL address
Duan Hanyi is " search ", " prices of X commodity ", and the result that web crawlers crawls that search and webpage returns is " Y dollar ", then described
DAF 100 sets up the corresponding relation of these search key section hunting actions, X commodity price and Y dollar.
In the another embodiment of the present embodiment, the step that web page contents rule base is set up in search includes：
Sampling is carried out to the original signaling data of described online and obtains original signaling data sample of surfing the Net.
The original signaling data of described online obtaining in sampling selecting step S110, obtains the original signaling data of described online
Sample.
User operation in the reduction original signaling data sample of online or web page contents, and obtain the original signaling of described online
The critical field of URL address in data sample.
Described original signaling data of surfing the Net includes the web site contents of user's link, by the original signaling data of described online
Reduction can obtain the user operation in webpage or web page contents.
Described user operation or web page contents are preserved to described web page contents rule with the corresponding relation of this critical field
Storehouse.
In sub-step S141, described DAF 100 according in step S130 obtain described critical field,
Described web page contents rule match described user operation or web page contents accordingly.
Sub-step S142, according to described user operation or web page contents, in conjunction with the described use surfed the Net in original signaling data
Family personal information, obtains the internet behavior of user.
User's online terminal used, online position, online application used is also comprised in described original signaling data of surfing the Net
The information such as software, intranet network type or Internet user's identity, in conjunction with above- mentioned information with webpage described user operation or
Web page contents, obtain the internet behavior of described user.
Refer to Fig. 5, a kind of mobile big data resolver 110 that Fig. 5 provides for the present embodiment, it is applied to mobile whole
The internet behavior of end subscriber carries out big data analysis, and described device includes：
Original signaling data acquisition module 111, the original signaling data of the online for gathering user；
Deep-packet detection module 112, for being carried out to the original signaling data of described online using deep packet inspection technical
Parsing, obtains the URL address that mobile phone users online accesses；
URL address depth analysis module 113, for being classified and depth analysis to described URL address, from described URL
Address obtains critical field；
Internet behavior analysis module 114, for carrying out text mining according to described critical field, obtains the upper of user
Net behavior, wherein, described internet behavior includes surf the Net terminal used, online position, application software, web page operation used by online
Behavior type, browsing content type, intranet network type or Internet user's identity.
Specifically, in the present embodiment, described internet behavior analysis module 114 includes：
Webpage matched sub-block 1141, for critical field according to the content of described critical field in default web page contents
Mate corresponding user operation or web page contents in rule base, described web page contents rule base is included described user operation or
Web page contents and the corresponding relation of described keyword；
Content analysis submodule 1142, for obtaining the internet behavior of user according to described user operation or web page contents.
Specifically, in the present embodiment, described device also includes：
Web page contents rule base sets up module 115, is used for setting up described web page contents rule base, wherein, in described webpage
Then stock contains critical field and the described user operation in this corresponding webpage in URL address or web page contents in URL address to content regulation
Matching relationship.
Specifically, in the present embodiment, described web page contents rule base is set up module 115 and is additionally operable to：
Described URL address is carried out with sampling and obtains URL address sample, described acquisition URL address is crawled by web crawlers
The corresponding info web of sample, user operation or web page contents that storage user executes on this webpage, and set up described URL ground
In location, critical field and the corresponding relation of described user operation or web page contents, preserve described corresponding relation to described web page contents
Rule base.
Specifically, in the present embodiment, described web page contents rule base is set up mould and is additionally operable to：
Sampling is carried out to the original signaling data of described online and obtains original signaling data sample of surfing the Net；The reduction original letter of online
Make the user operation in data sample or web page contents, and obtain the pass of URL address in described original signaling data sample of surfing the Net
Key field；Described user operation or web page contents are preserved to described web page contents rule with the corresponding relation of this critical field
Storehouse.
In sum, a kind of mobile big data analysis method and device that the present invention provides, deep packet inspection technical is to institute
The carrying out stating original signaling data of surfing the Net parses, and obtains the URL address that mobile phone users online accesses.And pass through described URL
Keyword in address mates user operation or web page contents in webpage in default described web page contents rule base, with this
Analysis obtains the internet behavior of user.So, can more comprehensive and accurate reflection user's online behavior, and amount of calculation is more
Little, the data statistic analysis for the later stage provide support.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.All within the spirit and principles in the present invention, made any repair
Change, equivalent, improvement etc., should be included within the scope of the present invention.
Claims (10)
1. a kind of mobile big data analytic method, is applied to the internet behavior to mobile phone users and carries out big data analysis, its
It is characterised by, methods described includes：
The original signaling data of online of collection user；
Using deep packet inspection technical, described the carrying out surfing the Net original signaling data is parsed, obtain mobile phone users online and visit
The URL address asked；
Is classified and depth analysis in described URL address, obtained critical field from described URL address；
Text mining is carried out according to described critical field, and combines the described individual subscriber surfed the Net in original signaling data letter
Breath, the internet behavior of acquisition user, wherein, described internet behavior includes answering used by online terminal used, online position, online
With software, web page operation behavior type, the content browsing webpage, intranet network type or Internet user's identity.
2. method according to claim 1 is it is characterised in that described carry out web page contents according to described critical field and divide
Analysis, and combine the described userspersonal information surfing the Net in original signaling data, the step obtaining the internet behavior of user includes：
Content according to described critical field is mated in corresponding user operation or webpage in default web page contents rule base
Hold, described web page contents rule base is included with the corresponding relation of described user operation or web page contents and described critical field；
According to described user operation or web page contents, in conjunction with the described userspersonal information surfing the Net in original signaling data, obtain
The internet behavior of user.
3. method according to claim 2 is it is characterised in that methods described also includes：
Set up described web page contents rule base, wherein, described web page contents rule base be stored with URL address critical field with should
Described user operation in the corresponding webpage in URL address or the matching relationship of web page contents.
4. method according to claim 3 is it is characterised in that the described step setting up described web page contents rule base is also wrapped
Include：
Described URL address is carried out with sampling and obtains URL address sample, described acquisition URL address sample is crawled by web crawlers
Corresponding info web, user operation or web page contents that storage user executes on this webpage, and set up in described URL address
Critical field and the corresponding relation of described user operation or web page contents, preserve described corresponding relation to described web page contents rule
Storehouse.
5. method according to claim 3 is it is characterised in that the described step setting up described web page contents rule base is also wrapped
Include：
Sampling is carried out to the original signaling data of described online and obtains original signaling data sample of surfing the Net；
User operation in the reduction original signaling data sample of online or web page contents, and obtain the original signaling data of described online
The critical field of URL address in sample；
Described user operation or web page contents are preserved to described web page contents rule base with the corresponding relation of this critical field.
6. a kind of mobile big data resolver, is applied to the internet behavior to mobile phone users and carries out big data analysis, its
It is characterised by, described device includes：
Original signaling data acquisition module, the original signaling data of the online for gathering user；
Deep-packet detection module, for being parsed to described the carrying out surfing the Net original signaling data using deep packet inspection technical, is obtained
Obtain the URL address that mobile phone users online accesses；
URL address depth analysis module, for being classified and depth analysis to described URL address, obtains from described URL address
Critical field；
Internet behavior analysis module, for carrying out text mining according to described critical field, in conjunction with the original letter of described online
Make the userspersonal information in data, obtain the internet behavior of user, wherein, described internet behavior includes end used of surfing the Net
End, online position, online used by application software, web page operation behavior type, the content browsing webpage, intranet network type or on
Network users identity.
7. device according to claim 6 is it is characterised in that described internet behavior analysis module includes：
Webpage matched sub-block, for critical field according to the content of described critical field in default web page contents rule base
Mate corresponding user operation or web page contents, described user operation or web page contents are included to described web page contents rule base
Corresponding relation with described keyword；
Content analysis submodule, for according to described user operation or web page contents, in conjunction with the original signaling data of described online
Userspersonal information, obtain user internet behavior.
8. device according to claim 7 is it is characterised in that described device also includes：
Web page contents rule base sets up module, is used for setting up described web page contents rule base, wherein, described web page contents rule base
The critical field in URL address that is stored with is closed with the coupling of the described user operation in this corresponding webpage in URL address or web page contents
System.
9. device according to claim 8 is it is characterised in that described web page contents rule base is set up module and is additionally operable to：
Described URL address is carried out with sampling and obtains URL address sample, described acquisition URL address sample is crawled by web crawlers
Corresponding info web, user operation or web page contents that storage user executes on this webpage, and set up in described URL address
Critical field and the corresponding relation of described user operation or web page contents, preserve described corresponding relation to described web page contents rule
Storehouse.
10. device according to claim 8 is it is characterised in that described web page contents rule base is set up mould and is additionally operable to：
Sampling is carried out to the original signaling data of described online and obtains original signaling data sample of surfing the Net；The reduction original signaling number of online
According to the user operation in sample or web page contents, and obtain the keyword of URL address in described original signaling data sample of surfing the Net
Section；Described user operation or web page contents are preserved to described web page contents rule base with the corresponding relation of this critical field.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610828017.5A CN106446113A (en) | 2016-09-18 | 2016-09-18 | Mobile big data analysis method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610828017.5A CN106446113A (en) | 2016-09-18 | 2016-09-18 | Mobile big data analysis method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106446113A true CN106446113A (en) | 2017-02-22 |
Family
ID=58168509
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610828017.5A Pending CN106446113A (en) | 2016-09-18 | 2016-09-18 | Mobile big data analysis method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106446113A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107257390A (en) * | 2017-05-27 | 2017-10-17 | 北京思特奇信息技术股份有限公司 | A kind of parsing method and system of URL addresses |
| CN107766214A (en) * | 2017-10-12 | 2018-03-06 | 南京熊猫电子股份有限公司 | Mobile terminal data method for stream processing and system based on SPARK technologies |
| CN108205592A (en) * | 2017-12-30 | 2018-06-26 | 中国移动通信集团江苏有限公司 | User's online signaling content analytic method, device, equipment and storage medium |
| CN108255993A (en) * | 2017-12-29 | 2018-07-06 | 北京三快在线科技有限公司 | Extract method, apparatus, electronic equipment and the storage medium of service fields |
| CN108363655A (en) * | 2018-02-11 | 2018-08-03 | 百度在线网络技术（北京）有限公司 | User behavior characteristics analysis method and device |
| CN109634947A (en) * | 2018-12-19 | 2019-04-16 | 深圳创维数字技术有限公司 | A kind of data processing method, system, equipment and medium |
| CN109858901A (en) * | 2019-01-28 | 2019-06-07 | 重庆邮电大学 | Mobile subscriber based on mobile phone signaling big data pays recognition methods |
| CN111817917A (en) * | 2020-07-03 | 2020-10-23 | 中移（杭州）信息技术有限公司 | Deep packet inspection method, device, server and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103646119A (en) * | 2013-12-26 | 2014-03-19 | 北京西塔网络科技股份有限公司 | Method and device for generating user behavior record |
| EP2857987A1 (en) * | 2012-06-30 | 2015-04-08 | Huawei Technologies Co., Ltd. | Acquiring method, device and system of user behavior |
| CN105786965A (en) * | 2016-01-27 | 2016-07-20 | 久远谦长（北京）技术服务有限公司 | URL-based user behavior analysis method and device |
-
2016
- 2016-09-18 CN CN201610828017.5A patent/CN106446113A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2857987A1 (en) * | 2012-06-30 | 2015-04-08 | Huawei Technologies Co., Ltd. | Acquiring method, device and system of user behavior |
| CN103646119A (en) * | 2013-12-26 | 2014-03-19 | 北京西塔网络科技股份有限公司 | Method and device for generating user behavior record |
| CN105786965A (en) * | 2016-01-27 | 2016-07-20 | 久远谦长（北京）技术服务有限公司 | URL-based user behavior analysis method and device |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107257390A (en) * | 2017-05-27 | 2017-10-17 | 北京思特奇信息技术股份有限公司 | A kind of parsing method and system of URL addresses |
| CN107766214A (en) * | 2017-10-12 | 2018-03-06 | 南京熊猫电子股份有限公司 | Mobile terminal data method for stream processing and system based on SPARK technologies |
| CN108255993A (en) * | 2017-12-29 | 2018-07-06 | 北京三快在线科技有限公司 | Extract method, apparatus, electronic equipment and the storage medium of service fields |
| CN108205592A (en) * | 2017-12-30 | 2018-06-26 | 中国移动通信集团江苏有限公司 | User's online signaling content analytic method, device, equipment and storage medium |
| CN108363655A (en) * | 2018-02-11 | 2018-08-03 | 百度在线网络技术（北京）有限公司 | User behavior characteristics analysis method and device |
| CN108363655B (en) * | 2018-02-11 | 2021-07-13 | 百度在线网络技术（北京）有限公司 | User behavior characteristic analysis method and device |
| CN109634947A (en) * | 2018-12-19 | 2019-04-16 | 深圳创维数字技术有限公司 | A kind of data processing method, system, equipment and medium |
| CN109858901A (en) * | 2019-01-28 | 2019-06-07 | 重庆邮电大学 | Mobile subscriber based on mobile phone signaling big data pays recognition methods |
| CN111817917A (en) * | 2020-07-03 | 2020-10-23 | 中移（杭州）信息技术有限公司 | Deep packet inspection method, device, server and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106446113A (en) | Mobile big data analysis method and device | |
| CN107341399B (en) | Method and device for evaluating security of code file | |
| US20150295942A1 (en) | Method and server for performing cloud detection for malicious information | |
| KR100848319B1 (en) | Harmful web site filtering method and apparatus using web structural information | |
| CN104063401B (en) | The method and apparatus that a kind of webpage pattern address merges | |
| WO2014180130A1 (en) | Method and system for recommending contents | |
| CN103714119B (en) | A kind for the treatment of method and apparatus of browser data | |
| CN102436564A (en) | Method and device for identifying falsified webpage | |
| CN102004770A (en) | Webpage auditing method and device | |
| CN102647408A (en) | Method for judging phishing website based on content analysis | |
| US20140331142A1 (en) | Method and system for recommending contents | |
| CN105868290B (en) | Method and device for displaying search results | |
| CN103297469A (en) | Method and device of collecting website data | |
| CN106446115A (en) | Mobile Internet user classification method and device | |
| EP3289487B1 (en) | Computer-implemented methods of website analysis | |
| US20150120692A1 (en) | Method, device, and system for acquiring user behavior | |
| CN103338260A (en) | Distributed analytical system and analytical method for URL logs in network auditing | |
| CN110020161B (en) | Data processing method, log processing method and terminal | |
| KR20180074774A (en) | How to identify malicious websites, devices and computer storage media | |
| CN106503111B (en) | Webpage code-transferring method, device and client terminal | |
| CN103778156A (en) | Method and device for searching for data and server for data search | |
| CN104317884A (en) | Method and device for acquiring types of source pages of website | |
| KR101430175B1 (en) | System and method for searching leakage of individual information | |
| CN104778232B (en) | Searching result optimizing method and device based on long query | |
| CN104182402A (en) | Browser interface address bar input control method and browser interface address bar input control system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170222 |